inst/markdown/archive/feeding_networks_report.md
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
title: "Snow crab predators"
author: "Jae S. Choi"
toc: true
number-sections: true
highlight-style: pygments
editor:
render-on-save: false
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html:
code-fold: true
html-math-method: katex
self-contained: true
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docx: default
First set up environment and get the data.
#| eval: true
#| output: false
#| warning: false
#| error: false
# startup libraries and directories
require(aegis)
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
project_directory = p$data_root
data_dir = file.path( project_directory, "data", "diets" )
media_dir = file.path( project_directory, "media" )
require(data.table) # for speed
require(lubridate)
require(stringr)
require(gt) # table formatting
require(ggplot2)
require(aegis) # map-related
require(bio.taxonomy) # handle species codes
# assimilate the CSV data tables:
# diet = get_feeding_data( data_dir, redo=TRUE ) # if there is a data update
diet = get_feeding_data( data_dir, redo=FALSE )
What are the predators of snow crab?
#| eval: true
#| output: true
#| label: predator_list
#| tbl-cap: "All predators of snow crab on Scotian Shelf of Atlantic Canada. Of 58287 finfish stomach samples, 159 had snow crab (0.28%). There is no indormation on snow crab diet in the database."
tx = taxa_to_code("snow crab")
# matching codes are
# spec tsn tx vern tx_index
#1 528 172379 BENTHODESMUS BENTHODESMUS 1659
#2 2522 98427 CHIONOECETES SPIDER QUEEN SNOW UNID 728
#3 2526 98428 CHIONOECETES OPILIO SNOW CRAB QUEEN 729
# 2 and 3 are correct
snowcrab_predators = diet[ preyspeccd %in% c(2522, 2526), ] # n=159 oservations out of a total of 58287 observations in db (=0.28% of all data)
snowcrab_predators$Species = code_to_taxa(snowcrab_predators$spec)$vern
snowcrab_predators$Predator = factor(snowcrab_predators$Species)
counts = snowcrab_predators[ , .(Frequency=.N), by=.(Species)]
setorderv(counts, "Frequency", order=-1)
gt::gt(counts) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
Where are the predators with snow crab in their stomachs?
#| eval: false
#| output: false
#| label: temp_depth
#| fig-cap: "Temperature and depths of snow crab predation on the Scotian Shelf of Atlantic Canada. Grey is all species observations in diet data base. Red is snow crab as prey."
#| fig-dpi: 144
#| fig-height: 4
#plot( depth ~ bottom_temperature, diet, pch=18, col="grey")
#points( depth ~ bottom_temperature, snowcrab_predators, col="red", pch=19 )
#| eval: true
#| output: true
#| label: map_observations_pre2010
#| fig-cap: "Locations where snow crab predation was observed on the Scotian Shelf of Atlantic Canada, 2000-2010."
#| fig-dpi: 144
#| fig-height: 6
ggplot() +
borders("world", fill = "lightgray", colour = "grey80") +
xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) +
geom_point(data=snowcrab_predators[year(timestamp) %in% c(2000:2010),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) +
labs(x="Longitude", y="Latitude", caption="2000-2010") +
theme(legend.position="inside", legend.position.inside =c(0.16, 0.65), legend.title=element_blank(), legend.text=element_text(size=6.0)
)
#| eval: true
#| output: true
#| label: map_observations_post2010
#| fig-cap: "Locations where snow crab predation was observed on the Scotian Shelf of Atlantic Canada, 2011-2020."
#| fig-dpi: 144
#| fig-height: 6
ggplot() +
borders("world", fill = "lightgray", colour = "grey80") +
xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) +
geom_point(data=snowcrab_predators[year(timestamp) %in% c(2011:2020),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) +
labs(x="Longitude", y="Latitude", caption="2011-2020") +
theme(legend.position="inside", legend.position.inside =c(0.16, 0.725), legend.title=element_blank(), legend.text=element_text(size=6.0) )
Project location of predators upon species composition information
Basic idea is that we have a reasonable undertanding of demersal communities. Based upon this, we can project where these predation events are most likely.
Note:
#| eval: false
#| output: true
# Loadings are eigenvectors scaled by the square roots of the respective eigenvalues
loadings = eigenvectors %*% sqrt(diag( eigenvalues, nrow = length( eigenvalues )))
# scores are sum of dot products
scores = indat %*% t(pracma::pinv(loadings ))
#| eval: true
#| output: true
#| warning: false
#| error: false
year.assessment = 2023
yrs = 1999:year.assessment
carstm_model_label="default"
require(aegis)
require(aegis.speciescomposition)
p = speciescomposition_parameters( yrs=yrs, carstm_model_label=carstm_model_label )
# speciescomposition_db( DS="speciescomposition.ordination.redo", p=p ) # analsysis
# speciescomposition_db( DS="speciescomposition.redo", p=p ) # compute planar coords and remove dups
pca = speciescomposition_db( DS="pca", p=p ) # analsysis
names(pca)
# [1] "cm" "indat" "rotate" "eigenvalues"
# [5] "eigenvectors" "loadings" "scores" "variance"
# [9] "variance_percent" "total_variance"
predator_names = unique(snowcrab_predators$Species)
pcares = as.data.frame( pca$loadings )
pcares$vern = taxonomy.recode( from="spec", to="taxa", tolookup=rownames( pcares ) )$vern
pcares$sc_predator = 0
pcares$sc_predator[ which(pcares$vern %in% predator_names) ] = 1
toblank = which( pcares$sc_predator==0)
toblank = setdiff( toblank, which(pcares$vern=="SNOW CRAB QUEEN" ) )
pcares$label = pcares$vern
pcares$label[toblank] = "+"
plot( PC2 ~ PC1, pcares, type="n")
text( PC2 ~ PC1, labels=label, data=pcares, cex=0.8 )
pcares$uv = pcares$sc_predator
pcares$uv[ which( pcares$vern=="SNOW CRAB QUEEN")] = 1
setDT(pcares)
# re-weight species by frequency
setDT(counts)
counts[, weight := Frequency / sum(Frequency) ]
pcares$Species = pcares$vern
pcares = counts[pcares, on="Species"]
pcares$weight[ which(is.na(pcares$weight)) ] = 0
pcares$weight = pcares$weight * 3
pcares$weight[ pcares$Species =="SNOW CRAB QUEEN" ] = 3
# uvs =pcares$uv %*% t(pracma::pinv(pca$loadings ))
# points( uvs[2] ~ uvs[1], col="red" )
# segments(0, 0, x1 = uvs[1], y1 = uvs[2], col = "red", lwd = 2)
# uvsw = pcares$uv*pcares$weight %*% t(pracma::pinv(pca$loadings ))
# points( uvsw[2] ~ uvsw[1], col="green" )
# segments(0, 0, x1 = uvsw[1], y1 = uvsw[2], col = "green", lwd = 2)
Question: Where do these predators and snow crab co-occur?
Next use eigenvectors from community field and apply to individual predators's consumption patterns ... this identifies where along the axes they are focussing in terms of diet.
Plot distances along axes of key species.
Issues^1
Bibliography
jae0/snowcrab documentation built on Nov. 6, 2024, 10:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Snow crab predators" author: "Jae S. Choi" toc: true number-sections: true highlight-style: pygments editor: render-on-save: false format: html: code-fold: true html-math-method: katex self-contained: true embed-resources: true pdf: pdf-engine: lualatex docx: default
First set up environment and get the data.
#| eval: true
#| output: false
#| warning: false
#| error: false
# startup libraries and directories
require(aegis)
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
project_directory = p$data_root
data_dir = file.path( project_directory, "data", "diets" )
media_dir = file.path( project_directory, "media" )
require(data.table) # for speed
require(lubridate)
require(stringr)
require(gt) # table formatting
require(ggplot2)
require(aegis) # map-related
require(bio.taxonomy) # handle species codes
# assimilate the CSV data tables:
# diet = get_feeding_data( data_dir, redo=TRUE ) # if there is a data update
diet = get_feeding_data( data_dir, redo=FALSE )
What are the predators of snow crab?
#| eval: true
#| output: true
#| label: predator_list
#| tbl-cap: "All predators of snow crab on Scotian Shelf of Atlantic Canada. Of 58287 finfish stomach samples, 159 had snow crab (0.28%). There is no indormation on snow crab diet in the database."
tx = taxa_to_code("snow crab")
# matching codes are
# spec tsn tx vern tx_index
#1 528 172379 BENTHODESMUS BENTHODESMUS 1659
#2 2522 98427 CHIONOECETES SPIDER QUEEN SNOW UNID 728
#3 2526 98428 CHIONOECETES OPILIO SNOW CRAB QUEEN 729
# 2 and 3 are correct
snowcrab_predators = diet[ preyspeccd %in% c(2522, 2526), ] # n=159 oservations out of a total of 58287 observations in db (=0.28% of all data)
snowcrab_predators$Species = code_to_taxa(snowcrab_predators$spec)$vern
snowcrab_predators$Predator = factor(snowcrab_predators$Species)
counts = snowcrab_predators[ , .(Frequency=.N), by=.(Species)]
setorderv(counts, "Frequency", order=-1)
gt::gt(counts) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
Where are the predators with snow crab in their stomachs?
#| eval: false
#| output: false
#| label: temp_depth
#| fig-cap: "Temperature and depths of snow crab predation on the Scotian Shelf of Atlantic Canada. Grey is all species observations in diet data base. Red is snow crab as prey."
#| fig-dpi: 144
#| fig-height: 4
#plot( depth ~ bottom_temperature, diet, pch=18, col="grey")
#points( depth ~ bottom_temperature, snowcrab_predators, col="red", pch=19 )
#| eval: true
#| output: true
#| label: map_observations_pre2010
#| fig-cap: "Locations where snow crab predation was observed on the Scotian Shelf of Atlantic Canada, 2000-2010."
#| fig-dpi: 144
#| fig-height: 6
ggplot() +
borders("world", fill = "lightgray", colour = "grey80") +
xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) +
geom_point(data=snowcrab_predators[year(timestamp) %in% c(2000:2010),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) +
labs(x="Longitude", y="Latitude", caption="2000-2010") +
theme(legend.position="inside", legend.position.inside =c(0.16, 0.65), legend.title=element_blank(), legend.text=element_text(size=6.0)
)
#| eval: true
#| output: true
#| label: map_observations_post2010
#| fig-cap: "Locations where snow crab predation was observed on the Scotian Shelf of Atlantic Canada, 2011-2020."
#| fig-dpi: 144
#| fig-height: 6
ggplot() +
borders("world", fill = "lightgray", colour = "grey80") +
xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) +
geom_point(data=snowcrab_predators[year(timestamp) %in% c(2011:2020),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) +
labs(x="Longitude", y="Latitude", caption="2011-2020") +
theme(legend.position="inside", legend.position.inside =c(0.16, 0.725), legend.title=element_blank(), legend.text=element_text(size=6.0) )
Project location of predators upon species composition information
Basic idea is that we have a reasonable undertanding of demersal communities. Based upon this, we can project where these predation events are most likely.
Note:
#| eval: false
#| output: true
# Loadings are eigenvectors scaled by the square roots of the respective eigenvalues
loadings = eigenvectors %*% sqrt(diag( eigenvalues, nrow = length( eigenvalues )))
# scores are sum of dot products
scores = indat %*% t(pracma::pinv(loadings ))
#| eval: true
#| output: true
#| warning: false
#| error: false
year.assessment = 2023
yrs = 1999:year.assessment
carstm_model_label="default"
require(aegis)
require(aegis.speciescomposition)
p = speciescomposition_parameters( yrs=yrs, carstm_model_label=carstm_model_label )
# speciescomposition_db( DS="speciescomposition.ordination.redo", p=p ) # analsysis
# speciescomposition_db( DS="speciescomposition.redo", p=p ) # compute planar coords and remove dups
pca = speciescomposition_db( DS="pca", p=p ) # analsysis
names(pca)
# [1] "cm" "indat" "rotate" "eigenvalues"
# [5] "eigenvectors" "loadings" "scores" "variance"
# [9] "variance_percent" "total_variance"
predator_names = unique(snowcrab_predators$Species)
pcares = as.data.frame( pca$loadings )
pcares$vern = taxonomy.recode( from="spec", to="taxa", tolookup=rownames( pcares ) )$vern
pcares$sc_predator = 0
pcares$sc_predator[ which(pcares$vern %in% predator_names) ] = 1
toblank = which( pcares$sc_predator==0)
toblank = setdiff( toblank, which(pcares$vern=="SNOW CRAB QUEEN" ) )
pcares$label = pcares$vern
pcares$label[toblank] = "+"
plot( PC2 ~ PC1, pcares, type="n")
text( PC2 ~ PC1, labels=label, data=pcares, cex=0.8 )
pcares$uv = pcares$sc_predator
pcares$uv[ which( pcares$vern=="SNOW CRAB QUEEN")] = 1
setDT(pcares)
# re-weight species by frequency
setDT(counts)
counts[, weight := Frequency / sum(Frequency) ]
pcares$Species = pcares$vern
pcares = counts[pcares, on="Species"]
pcares$weight[ which(is.na(pcares$weight)) ] = 0
pcares$weight = pcares$weight * 3
pcares$weight[ pcares$Species =="SNOW CRAB QUEEN" ] = 3
# uvs =pcares$uv %*% t(pracma::pinv(pca$loadings ))
# points( uvs[2] ~ uvs[1], col="red" )
# segments(0, 0, x1 = uvs[1], y1 = uvs[2], col = "red", lwd = 2)
# uvsw = pcares$uv*pcares$weight %*% t(pracma::pinv(pca$loadings ))
# points( uvsw[2] ~ uvsw[1], col="green" )
# segments(0, 0, x1 = uvsw[1], y1 = uvsw[2], col = "green", lwd = 2)
Question: Where do these predators and snow crab co-occur?
Next use eigenvectors from community field and apply to individual predators's consumption patterns ... this identifies where along the axes they are focussing in terms of diet.
Plot distances along axes of key species.
Issues^1
Bibliography
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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